Although MDRN is an effective SID model, it is still difficult to reconstruct high-quality denoised images under extreme conditions, such as tiny models or severe noises.
Therefore, we propose two novel Heterogeneous Knowledge Distillation Strategies (HKDS) to enable MDRN to automatically learn the deep feature priors from the teacher model.
Different from previous works [1]}, [2]}, [3]} that learned the knowledge from different models or tasks, we explore more efficient knowledge distillation strategies from the model and the task itself.
